Table of Contents:
“...14 Certified robustness training -- 14.1 A framework for certified robust training -- 14.2 Existing algorithms and their performances -- Interval bound propagation (IBP) -- Linear relaxation-based training -- 14.3 Empirical comparison -- 14.4 Extended reading -- 15 Adversary detection -- 15.1 Detecting adversarial inputs -- 15.2 Detecting adversarial audio inputs -- 15.3 Detecting Trojan models -- 15.4 Extended reading -- 16 Adversarial robustness of beyond neural network models -- 16.1 Evaluating the robustness of K-nearest-neighbor models -- A primal-dual quadratic
programming formulation -- Dual quadratic
programming problems -- Robustness verification for 1-NN models -- Efficient algorithms for
computing 1-NN robustness -- Extending beyond 1-NN -- Robustness of KNN vs neural network on simple problems -- 16.2 Defenses with nearest-neighbor classifiers -- 16.3 Evaluating the robustness of decision tree ensembles -- Robustness of a single decision tree -- Robustness of ensemble decision stumps -- Robustness of ensemble decision trees -- Training robust tree ensembles -- 17 Adversarial robustness in meta-learning and contrastive learning -- 17.1
Fast adversarial robustness adaptation in model-agnostic meta-learning -- When and how to incorporate robust regularization in MAML? ...
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